用深度学习预测篮球投篮后的球权归属，提升实时转播与分析能力。
See It Before You Grab It: Deep Learning-based Action Anticipation in Basketball
- 基于广播视频构建动作预判模型，聚焦投篮后球权归属预测。
- 提出包含2000个标注回弹事件的10万段视频数据集，支持多任务研究。
- 首次实现篮球回弹预判的深度学习应用，适用于实时转播与战术分析。
计算机视觉与视频理解已推动体育分析的自动化发展，能够从转播画面中大规模分析比赛动态。尽管球员与球体追踪、姿态估计、动作定位及自动犯规识别取得显著进展，但对体育视频中动作发生的提前预判仍关注不足。本文提出篮球广播视频中的动作预判任务，重点在于预测投篮后哪支队伍将获得球权。为此，构建了一个自标注数据集，包含10万段篮球视频片段、300多小时的录像内容，以及超过2000个手动标注的篮板事件。采用先进的动作预判方法进行基准测试，首次实现深度学习在篮球篮板预测中的应用。同时探索了篮板分类与定位两个互补任务，证明该数据集可支持广泛的篮球视频理解应用，且目前尚无类似规模的数据集可用。实验结果表明，篮板预判具有可行性但也面临固有挑战，为动态多智能体体育场景的预测建模提供了宝贵洞见。通过在篮板发生前预测球权归属，本工作支持实时自动化转播与赛后分析工具，助力决策制定。
原文摘要 · Abstract (English)
Computer vision and video understanding have transformed sports analytics by enabling large-scale, automated analysis of game dynamics from broadcast footage. Despite significant advances in player and ball tracking, pose estimation, action localization, and automatic foul recognition, anticipating actions before they occur in sports videos has received comparatively little attention. This work introduces the task of action anticipation in basketball broadcast videos, focusing on predicting which team will gain possession of the ball following a shot attempt. To benchmark this task, a new self-curated dataset comprising 100,000 basketball video clips, over 300 hours of footage, and more than 2,000 manually annotated rebound events is presented. Comprehensive baseline results are reported using state-of-the-art action anticipation methods, representing the first application of deep learning techniques to basketball rebound prediction. Additionally, two complementary tasks, rebound classification and rebound spotting, are explored, demonstrating that this dataset supports a wide range of video understanding applications in basketball, for which no comparable datasets currently exist. Experimental results highlight both the feasibility and inherent challenges of anticipating rebounds, providing valuable insights into predictive modeling for dynamic multi-agent sports scenarios. By forecasting team possession before rebounds occur, this work enables applications in real-time automated broadcasting and post-game analysis tools to support decision-making.
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